[논문]Real-Time Peak Shaving Algorithm Using Fuzzy Wind Power Generation Curves for Large-Scale Battery Energy Storage Systems

Son, Subin; Song, Hwachang

doi:10.5391/ijfis.2014.14.4.305

Real-Time Peak Shaving Algorithm Using Fuzzy Wind Power Generation Curves for Large-Scale Battery Energy Storage Systems 원문보기

International journal of fuzzy logic and intelligent systems : IJFIS, v.14 no.4, 2014년, pp.305 - 312

Son, Subin (Department of Electrical and Information Engineering, Seoul Nationall University of Science & Technology) , Song, Hwachang (Department of Electrical and Information Engineering, Seoul Nationall University of Science & Technology)

Abstract ▼ AI-Helper

This paper discusses real-time peak shaving algorithms for a large-scale battery energy storage system (BESS). Although several transmission and distribution functions could be implemented for diverse purposes in BESS applications, this paper focuses on a real-time peak shaving algorithm for an energy time shift, considering wind power generation. In a high wind penetration environment, the effective load levels obtained by subtracting the wind generation from the load time series at each long-term cycle time unit are needed for efficient peak shaving. However, errors can exist in the forecast load and wind generation levels, and the real-time peak shaving operation might require a method for wind generation that includes comparatively large forecasting errors. To effectively deal with the errors of wind generation forecasting, this paper proposes a real-time peak shaving algorithm for threshold value-based peak shaving that considers fuzzy wind power generation.

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문제 정의

This paper presented long-term cycle control strategies for a large-scale BESS. The main focus of this paper was illustrating the applicability of a fuzzy model for the forecast wind power generation as an input for real-time peak shaving. This operation mode could be used to perform an energy time shift, and minimize the threshold value obtained from the difference between the upper and lower loads.
One solution might be the application of energy storage systems, which can play an important role in actively coping with the problem resulting from the variability of renewable energy resources, by storing energy that can later be consuming as needed. This paper focuses on the application of a battery energy storage system (BESS).
To effectively cope with the wind generation forecasting errors, this paper employs fuzzy wind power generation curves in a real-time algorithm for threshold value peak shaving. This paper includes illustrative examples to show the effectiveness of this real-time algorithm for peak shaving.
This paper presented long-term cycle control strategies for a large-scale BESS. The main focus of this paper was illustrating the applicability of a fuzzy model for the forecast wind power generation as an input for real-time peak shaving.

제안 방법

The facilities for the demonstration research with a 4 MW and 8 MWh BESS at ‘Jochun’ was tested in the long-term and short-term cycle operation modes.
In [9], a model predictive control method was employed to reduce the peak electricity demand in building climate control based on spot price forecasting. This paper describes a peak shaving method that determines a charging/discharging schedule to minimize the peak shaving threshold value, P_shave , considering the load data.
This operation mode could be used to perform an energy time shift, and minimize the threshold value obtained from the difference between the upper and lower loads. This paper included an illustrative example showing the performance of the algorithm with real load and wind power generation data using real, forecast, and fuzzy model-based curves with the developed real-time operation simulator.
This paper proposes a real-time peak shaving algorithm that considers wind power generation. As in [6], the algorithm using a particular level of wind penetration employs the effective load curves, which are obtained by subtracting the wind generation from the load time series at each long-term cycle time unit.
In Figures 7–9, the x-axis denotes the real-time unit (2 min). This paper reports a real-time peak shaving simulation that divides the long-term time unit into 10 sections because the fuzzy wind power generation changes as a result of the real wind power generation data at each point. In other words, Figures 7–9 show the combined peak shaving results at the corresponding real-time units.
This paper uses a 48 × 4 matrix that includes the end-points of the trapezoidal models at all the periods using historical power generation data with 30-min intervals.

이론/모형

However, it should be noted that there are errors in the forecast load and wind generation values, and the real-time peak shaving operation might require a wind generation method with comparatively large forecasting errors. To effectively cope with the wind generation forecasting errors, this paper employs fuzzy wind power generation curves in a real-time algorithm for threshold value peak shaving. This paper includes illustrative examples to show the effectiveness of this real-time algorithm for peak shaving.

참고문헌 (12)

Electric Power Research Institute, "Electricity energy storage technology options," Available http://www.epri.com/abstracts/pages/productabstract.aspx?ProductID000000000001020676
J. Eyer and G. Corey, Energy Storage for the Electricity Grid: Benefits and Market Potential Assessment Guide: A Study for the DOE Energy Storage Systems Program [SAND2010-0815], Albuquerque, NM: Sandia National Laboratories, 2010.
J. B. Goodenough, H. D. Abruna, and M. V. Buchanan, Basic Research Needs for Electrical Energy Storage. Report of the Basic Energy Sciences Workshop on Electrical Energy Storage [DOE/SC/BES-0702]: U.S. Department of Energy, 2007.
R. Lasseter and M. Erickson, Integration of Battery-Based Energy Storage Element in the CERTS Microgrid [DEFC02-06CH11350]: US Department of Energy, 2009.
H. Song, S. Ohn, S. Lee, and B. Jang, "Long-term cycle scheduling algorithms in power management system for MW-scale batteries," in Proceedings of the IEEE Vehicle Power and Propulsion Conference, Seoul, Korea, October 9-12, 2012, pp. 1006-1009. http://dx.doi.org/10.1109/VPPC.2012.6422754
A. Bar-Noy, M. Johnson, and O. Liu, "Peak shaving through resource buffering," in Approximation and Online Algorithms. Lecture Notes in Computer Science, Vol. 5426, E. Bampis and M. Skutella, Eds. Heidelberg: Springer Berlin, 2009, pp. 147-159. http://dx.doi.org/10.1007/978-3-540-93980-1_12

상세보기
S. Ohn, J. S. Kim, H. Song, and B. Chang, "Fuzzy LP based power network peak shaving algorithm," Journal of Korean Institute of Intelligent Systems, vol. 22, no. 6, pp. 754-760, Dec. 2012. http://dx.doi.org/10.5391/JKIIS.2012.22.6.754

원문보기 상세보기
S. B. Son, H. Song, and B. Chang, "Long-term cycle scheduling algorithms of peak-shaving for MW-scale battery energy storage system," in Proceedings of the International Smart Grid Conference & Exhibition, 2013, vol. 1, pp. 727-732.
F. Oldewurtel, A. Ulbig, A. Parisio, G. Andersson, and M. Morari, "Reducing peak electricity demand in building climate control using real-time pricing and model predictive control," in Proceedings of the 49th IEEE Conference on Decision and Control, Atlanta, GA, December 15-17, 2010, pp. 1927-1932. http://dx.doi.org/10.1109/CDC.2010.5717458
S. M. Ross, Introduction to Probability Models, 10th ed., Boston, MA: Academic Press, 2010.
J. Zhu, Optimization of Power System Operation, Piscataway, NJ: Wiley-IEEE, 2009.
J. Nazarko and W. Zalewski, "The fuzzy regression approach to peak load estimation in power distribution systems," IEEE Transactions on Power Systems, vol. 14, no. 3, pp. 809-814, Aug. 1999. http://dx.doi.org/10.1109/59.780890

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Real-Time Peak Shaving Algorithm Using Fuzzy Wind Power Generation Curves for Large-Scale Battery Energy Storage Systems 원문보기

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Real-Time Peak Shaving Algorithm Using Fuzzy Wind Power Generation Curves for Large-Scale Battery Energy Storage Systems 원문보기

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